We perform a comprehensive examination of the recursive, comparative predictive performance of a number of linear and non-linear models for UK stock and bond returns. We estimate Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STR) regime switching models,...

We perform a comprehensive examination of the recursive, comparative predictive performance of a number of linear and non-linear models for UK stock and bond returns. We estimate Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STR) regime switching models,...

We document strong U.S. stock and bond return predictability from several macroeconomic volatility series before 1982. Return predictability declined significantly during the Great Moderation in the post-1982 sample. Our empirical finding is robust to out-of-sample "real time" forecasts in terms...

We document strong U.S. stock and bond return predictability from several macroeconomic volatility series before 1982, and a significant decline in this predictability during the Great Moderation. These findings are robust to alternative empirical specifications and out-of-sample tests. We...

We build an equilibrium model to explain why stock return predictability concentrates in bad times. The key feature is that investors use different forecasting models, and hence assess uncertainty differently. As economic conditions deteriorate, uncertainty rises and investors' opinions...

This is the first paper in the DSGE literature to match key business cycle moments and long-run equity returns in a small open economy with production. These results are achieved by introducing four modifications to a standard real business cycle model: (1) borrowing and lending costs are...

We show that a business-cycle component of consumption growth (dubbed business-cycle consumption) with cycles between 2 and 4 years is effective in explaining the differences in risk premia across alternative test assets, including recently-proposed anomaly portfolios. We formalize the mapping...